Shadow removal method of soil surface image based on GAN used for estimation of farmland soil moisture content

Meng, Chao; Yang, Wei; Wang, Dong; Hao, Ziyuan; Li, Minzan

doi:10.1088/1361-6501/acd133

Cited by 1 publication

(1 citation statement)

References 46 publications

(61 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Soil organic matter is a key parameter to measure the degree of land degradation and soil fertility, and plays an important role * Author to whom any correspondence should be addressed. in sustainable agricultural development and environmental protection [1][2][3][4][5][6]. Organic matter not only improves soil microbial activity, but also provides crops with essential nutrients for growth [7,8].…”

Section: Introductionmentioning

confidence: 99%

Development of a vehicle-mounted soil organic matter detection system based on near-infrared spectroscopy and image information fusion

Cao,

Yang,

et al. 2024

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

In the practical application of farmland, the soil organic matter prediction model es-tablished by the traditional near-infrared spectroscopy is affected by factors such as soil texture, which leads to a serious decline in the accuracy of the model. To im-prove the robustness and prediction accuracy of the model, a prediction model based on near-infrared spectroscopy and image fusion is proposed. A 1D CNN organic matter prediction model (based on near-infrared spectroscopy) was established using eight characteristic wavelengths of extracted soil organic matter (932 nm, 999 nm, 1083 nm, 1191 nm, 1316 nm, 1356 nm, 1583 nm, and 1626 nm) as spectral infor-mation. A 2D CNN organic matter prediction model was established using soil RGB images as information. Based on the idea of model weight fusion, 1D CNN and 2D CNN models are fused. When using small convolutional kernels(3-layer convolu-tional kernel size: 3 * 3, 1 * 1, 1 * 1)and 1D-CNN: 2D-CNN = 6:4, the model has the highest prediction accuracy(R2=0.872). The optimal fusion model was embedded into the inspection system. The final laboratory and field testing results are as fol-lows: under laboratory conditions, the detection accuracy R2 of the 1D CNN predic-tion model, 2D CNN prediction model, and fusion model are 0.838, 0.781, and 0.869, respectively. The RMSE is 3.005, 3.546, and 2.678, respectively. The above experi-mental data indicates that the R2 of the fused model is more accurate compared to the model established with a single information. In the field test, the R2 detection accuracy of 1D CNN prediction model, 2D CNN prediction model and fusion model is 0.809, 0.731 and 0.835, respectively. The root mean square errors are 3.466, 3.828 and 2.973, respectively. The results show that this testing system can meet the needs of soil nutrient testing in farmland and provide guidance for precision agriculture management.

show abstract

Section: Introductionmentioning

confidence: 99%